Embodiments relate to manipulation of database data, and in particular, to domain knowledge driven semantic extraction systems.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Modern Business Intelligence (BI) systems combine large amounts of operational data with analytical tools, in order to present complex and competitive information to planners and decision makers. Such BI systems seek to improve timeliness and quality of inputs to the decision-making process, by providing information such as: capabilities available in the enterprise; state of the art; trends; and future market directions; technologies; regulatory environment; and competitor actions and implications of those actions.
Factors such as the emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the evolution of web architecture, have each combined to create an enriched business intelligence environment. However, in the era of “big data”, rapid growth of data volume and complexity underscores the importance of precisely acquiring meaningful information from the data.
Specifically, the large volumes of data available from different business domains in heterogeneous structures or metrics, tends to exhibit bewildering complexity beyond the expertise of an average business customer. Such an average business customer would have difficulty in acquiring professional knowledge in different business domains. But, in most cases, assistance from domain experts is expensive or unavailable.
Thus, a fundamental challenge exists in extracting information that fits the insights of domain knowledge, and then delivering that information in a manner useful to an average business customer.
Accordingly, there is a need for domain knowledge driven semantic extraction systems.